Metadata-Version: 2.1
Name: amap
Version: 0.1.20
Summary: Automated mouse atlas propagation
Home-page: UNKNOWN
Author: Adam Tyson, Charly Rousseau, Christian Niedworok
Author-email: adam.tyson@ucl.ac.uk
License: UNKNOWN
Project-URL: Source Code, https://github.com/SainsburyWellcomeCentre/amap-python
Project-URL: Bug Tracker, https://github.com/SainsburyWellcomeCentre/amap-python/issues
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        # amap-python
        Automated mouse atlas propagation
        
        
        ## About
        amap is python software for registration of brain templates to sample whole-brain
        microscopy datasets, and subsequent atlas-based segmentation by
        [Adam Tyson](https://github.com/adamltyson), 
        [Charly Rousseau](https://github.com/crousseau) & 
        [Christian Niedworok](https://github.com/cniedwor) 
        from the [Margrie Lab at the Sainsbury Wellcome Centre](https://www.sainsburywellcome.org/web/groups/margrie-lab).
        
        
        This is a Python port of 
        [aMAP](https://github.com/SainsburyWellcomeCentre/aMAP/wiki) (originally 
        written in Java), which has been 
        [validated against human segmentation](https://www.nature.com/articles/ncomms11879).
        
        The actual registration is carried out by [NiftyReg](http://cmictig.cs.ucl.ac.uk/wiki/index.php/NiftyReg).
        
        ## Details
        The aim of amap is to register the template brain
         (e.g. from the [Allen Reference Atlas](https://mouse.brain-map.org/static/atlas))
          to the sample image. Once this is complete, any other image in the template
          space can be aligned with the sample (such as region annotations, for 
          segmentation of the sample image). The template to sample transformation
          can also be inverted, allowing sample images to be aligned in a common 
          coordinate space.
          
        To do this, the template and sample images are filtered, and then registered in 
        a three step process (reorientation, affine registration, and freeform 
        registration.) The resulting transform from template to standard space is then
        applied to the atlas. 
         
        Full details of the process are in the 
        [original paper](https://www.nature.com/articles/ncomms11879).
        ![process](https://raw.githubusercontent.com/SainsburyWellcomeCentre/amap-python/master/resources/reg_process.png)
        **Overview of the registration process**
        
        ## Installation
        ```bash
        pip install amap
        ```
        
        ## Usage
        amap was designed with generality in mind, but is currently used for a single application. If anyone has different uses 
        (e.g. requires a different atlas, or the data is in a different format), please get in touch 
        by [email](mailto:adam.tyson@ucl.ac.uk?subject=amap) or by 
        [raising an issue](https://github.com/SainsburyWellcomeCentre/amap-python/issues/new/choose).
        
        ### Basic usage
        ```bash
        amap /path/to/raw/data /path/to/output/directory -x 2 -y 2 -z 5
        ```
        
        ### Arguments
        #### Mandatory
        * Path to the directory of the images. 
        Can also be a text file pointing to the files.  
         * Output directory for all intermediate and final 
        results
        
        **Either**
        * `-x` or `--x-pixel-um` Pixel spacing of the data in the first dimension, 
        specified in um.
        * `-y` or `--y-pixel-um` Pixel spacing of the data in the second dimension, 
        specified in um.
        * `-z` or `--z-pixel-um` Pixel spacing of the data in the third dimension, 
        specified in um.
        
        **Or**
        * `--metadata` Metadata file containing pixel sizes (any format supported 
        by [micrometa](https://github.com/adamltyson/micrometa) can be used).
          If both pixel sizes and metadata are provided, the command line arguments 
          will take priority.
        
        #### Additional options
        * `-d` or `--downsample` Paths to N additional channels to downsample to the 
        same coordinate space.
        
        Full command-line arguments are available with `amap -h`, but please 
        [get in touch](mailto:adam.tyson@ucl.ac.uk?subject=amap) if you have any questions.
        
        
        ## Citing amap.
        
        If you find amap useful, and use it in your research, please cite the [original Nature Communications paper](https://www.nature.com/articles/ncomms11879) along with this repository:
        
        > Adam L. Tyson, Charly V. Rousseau, Christian J. Niedworok and Troy W. Margrie (2019). amap: automatic atlas propagation. [doi:10.5281/zenodo.3582162](https://zenodo.org/record/3582162)
        
        
        ## Visualisation
        amap has a built in visualisation function (built using [napari](https://github.com/napari/napari)).
        
        #### Usage
        ```bash
        amap_vis /path/to/amap/output/directory
        ```
        
        #### Mandatory
        * Path to amap output directory
        
        
        #### Additional options
        * `-r` or `--raw`. Rather than viewing the downsampled data, view the raw data 
        at full resolution. This will stream image planes as required, and so may be 
        slow.
        * `-c` or `--raw-channels` Paths to N additional channels to view. 
        Will only work if using the raw image viewer.
        
        N.B. If you have a high-resolution monitor, the scaling of the viewer may not work,
        this is a [known napari issue](https://github.com/napari/napari/issues/367).
        
        ![amap_viewer](https://raw.githubusercontent.com/SainsburyWellcomeCentre/amap-python/master/resources/amap_vis.gif)
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows :: Windows 10
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.6, <3.8
Description-Content-Type: text/markdown
Provides-Extra: dev
